ai-system-design-guide

What problem does it solve?

It solves the problem of designing production-grade AI systems by consolidating practical architecture patterns, RAG/agent engineering guidance, and staff-level interview preparation into one continually updated reference.

Core Features & Use Cases

• Production AI system design: end-to-end guidance for building reliable AI services, including multi-tenant isolation, security, and reliability patterns.
• RAG architecture engineering: covers chunking, vector retrieval, reranking, contextual retrieval, and late-interaction approaches like ColBERT.
• Agentic and tool-use workflows: explains MCP-enabled agent design, tool-use orchestration, and computer agents with safety considerations.
• Evaluation and observability: provides guidance on eval pipelines, monitoring, and quality gates for continuous improvement.

Quick Start

Ask the AI assistant to create a staff-level architecture plan for a multi-tenant RAG system with eval and monitoring, including the retrieval strategy, security boundaries, and a concrete implementation checklist.